ce_estimate_rams_att: Causal inference with multiple treatments using RAMS for ATT...

In CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome

Causal inference with multiple treatments using RAMS for ATT effects

Description

The function ce_estimate_rams_att implements RAMS to estimate ATT effect with multiple treatments using observational data.

Usage

ce_estimate_rams_att(y, w, x, method, reference_trt, ...)

Arguments

y	A numeric vector (0, 1) representing a binary outcome.
w	A numeric vector representing the treatment groups.
x	A dataframe, including all the covariates but not treatments.
method	A character string. Users can selected from the following methods including "RAMS-Multinomial", "RAMS-GBM", "RAMS-SL".
reference_trt	A numeric value indicating reference treatment group for ATT effect.
...	Other parameters that can be passed through to functions.

Value

A summary of the effect estimates can be obtained with summary function.

References

Matthew Cefalu, Greg Ridgeway, Dan McCaffrey, Andrew Morral, Beth Ann Griffin and Lane Burgette (2021). twang: Toolkit for Weighting and Analysis of Nonequivalent Groups. R package version 2.5. URL:https://CRAN.R-project.org/package=twang

Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0

Noah Greifer (2021). WeightIt: Weighting for Covariate Balance in Observational Studies. R package version 0.12.0. URL:https://CRAN.R-project.org/package=WeightIt

Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36

CIMTx documentation built on June 24, 2022, 9:07 a.m.